id: 01905015
dt: j
an: 01905015
au: Dall’Osso, Aldo
ti: A least squares method in back fitting the data base of a simulation model.
so: Adv. Eng. Softw. 33, No. 11-12, 743-748 (2002).
py: 2002
pu: Elsevier Science Ltd., Oxford
la: EN
cc: I.6.3 I.2.6
ut: Parameter identification; Best fit; Inverse problems; Adjustment to
    measurements; Data estimation; Model reset
ci: 
li: doi:10.1016/S0965-9978(02)00091-1
ab: Summary: This article presents an application of the least squares method
    in a particular class of inverse problems. Knowing the solution from
    experimental measurements, what are the corrections we must apply to
    the data of the problem in order to make the result close as much as
    possible to it? Normally the data of the problem are state functions
    known with a given degree of precision and in solving this problem the
    precision can be enhanced. This kind of problem is felt in many fields
    of engineering and physics, where an adjustment of a mathematical model
    on experimental observation is needed. This article shows a method to
    determine some residual function to be added to the data in order to
    refine the predictive power of the numerical model. An example is shown
    in a simple but concrete application.
rv: